A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment
DOI:
https://doi.org/10.13052/jwe1540-9589.20813Keywords:
5G, path planning, ant colony algorithm, reinforcement learning, path correctionAbstract
Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.
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Y. Deng, Y. Chen, Y. Zhang, S. Mahadevan, ‘Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment’, Applied Soft Computing, pp. 1231–1237, Mar., 2012.
X. Xu, X. Li, A. Zhan, ‘An enhanced path planning method for unmanned surface vehicle based on JPS+ and goalbounding algorithm’, IOP Conf. Ser.: Mater. Sci. Eng., p. 052079, Aug., 2019.
A. K. Guruji, H. Agarwal, D. K. Parsediya, ‘Time-efficient A* Algorithm for Robot Path Planning’, Procedia Technology, pp. 144–149, 2016.
B. F. Zhang, Y. C. Wang, X. L. Zhang, ‘Mobile Robot Path Planning Based on Artificial Potential Field Method’, AMM, pp. 350–353, Jul. 2014.
M. Dorigo, L. M. Gambardella, ‘Ant colonies for the travelling salesman problem’, Biosystems, pp. 73–81, Jul., 1997.
P. K. Das, P. K. Jena, ‘Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators’, Applied Soft Computing, p. 106312, Jul., 2020.
H.-Y. Lee, H. Shin, J. Chae, ‘Path Planning for Mobile Agents Using a Genetic Algorithm with a Direction Guided Factor’, Electronics, p. 212, Sep., 2018.
A. H. Karami, M. Hasanzadeh, ‘An adaptive genetic algorithm for robot motion planning in 2D complex environments’, Computers & Electrical Engineering, pp. 317–329, Apr., 2015.
W. Zhang, X. Gong, G. Han, Y. Zhao, ‘An Improved Ant Colony Algorithm for Path Planning in One Scenic Area With Many Spots’, IEEE Access, pp. 13260–13269, 2017.
Q. Song, Q. Zhao, S. Wang, Q. Liu, X. Chen, ‘Dynamic Path Planning for Unmanned Vehicles Based on Fuzzy Logic and Improved Ant Colony Optimization’, IEEE Access, pp. 62107–62115, 2020.
P. Wang, S. Gao, L. Li, B. Sun, S. Cheng, ‘Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm’, Energies, p. 2342, Jun., 2019.
M. Dorigo, V. Maniezzo, A. Colorni, ‘Ant System: An Autocatalytic Optimizing Process’, p. 21, 1991.
M. Dorigo, G. D. Caro, L. M. Gambardella, ‘Ant Algorithms for Discrete Optimization’, Artificial Life, pp. 137–172, Apr. 1999.
M. Yousefikhoshbakht, F. Didehvar, F. Rahmati, ‘An Efficient Solution for the VRP by Using a Hybrid Elite Ant System’, INT J COMPUT COMMUN, p. 340, Apr., 2014.
Z. Jiao, K. Ma, Y. Rong, P. Wang, H. Zhang, S. Wang, ‘A path planning method using adaptive polymorphic ant colony algorithm for smart wheelchairs’, Journal of Computational Science, pp. 50–57, Mar., 2018.
M. Dorigo, L. M. Gambardella, ‘Ant colony system: a cooperative learning approach to the traveling salesman problem’, IEEE Trans. Evol. Computat., pp. 53–66, Apr., 1997.
A. C. Zecchin, H. R. Maier, A. R. Simpson, M. Leonard, J. B. Nixon, ‘Ant Colony Optimization Applied to Water Distribution System Design: Comparative Study of Five Algorithms’, J. Water Resour. Plann. Manage., vol. 133, pp. 87–92, Jan., 2007.
Q. Luo, H. Wang, Y. Zheng, J. He, ‘Research on path planning of mobile robot based on improved ant colony algorithm’, Neural Comput & Applic, pp. 1555–1566, Mar., 2020.
X. Wu, G. Wei, Y. Song, X. Huang, ‘Improved ACO-based path planning with rollback and death strategies’, Systems Science & Control Engineering, pp. 102–107, Jan., 2018.
Y. Yao, Q. Ni, Q. Lv, K. Huang, ‘A novel heterogeneous feature ant colony optimization and its application on robot path planning’, in 2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, 522–528, May, 2015.
J. Liu, J. Yang, H. Liu, X. Tian, M. Gao, ‘An improved ant colony algorithm for robot path planning’, Soft Comput, pp. 5829–5839, Oct., 2017.
J. Zhao, D. Cheng, C. Hao, ‘An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle’, Mathematical Problems in Engineering, vol. pp. 1–10, 2016.
G. Xia, Z. Han, B. Zhao, C. Liu, X. Wang, ‘Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm’, Mathematical Problems in Engineering, pp. 1–10, Apr., 2019.
K. Akka, F. Khaber, ‘Mobile robot path planning using an improved ant colony optimization’, International Journal of Advanced Robotic Systems, p. 172988141877467, May, 2018.
W. Zhaowei, H. Weihua, Y. Wenkai, Z. Zheng, ‘Path Planning Based on Ant Colony Algorithm with Bayesian Decision-making’, Modular Machine Tool & Automatic Manufacturing Technique, pp. 41–46, 2020.
X. Dai, S. Long, Z. Zhang, D. Gong, ‘Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method’, Front. Neurorobot., Apr., 2019.
L. Hai, Y. Xiaoliu, X. Linghua, ‘Robot Navigation Path Planning Under Honeycomb Grid Based on Dynamic Grouping Ant Colony Algorithm’, Machinery Design & Manufacture, pp. 279–283,287, 2020.
Y. Noguchi, T. Maki, ‘Path Planning Method Based on Artificial Potential Field and Reinforcement Learning for Intervention AUVs’, 2019 IEEE Underwater Technology (UT), pp. 1–6, 2019.
F. Liu, C. Chen, Z. Li, Z.-H. Guan, and H. O. Wang, ‘Research on path planning of robot based on deep reinforcement learning’, 2020 39th Chinese Control Conference (CCC), 2020, pp. 3730–3734.
C. Qu, W. Gai, M. Zhong, J. Zhang, ‘A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning’, Applied Soft Computing, vol. 89, p. 106099, Apr., 2020.
F. Leiva, J. Ruiz-del-Solar, ‘Robust RL-Based Map-Less Local Planning: Using 2D Point Clouds as Observations’, IEEE Robot. Autom. Lett., pp. 5787–5794, Oct., 2020.
F. Jian, F. MinRui, M. ShiWei, ‘RL-ART2 Neural Network Based Mobile Robot Path Planning’, Sixth International Conference on Intelligent Systems Design and Applications, pp. 581–585, 2006.
J. Ming, W. Fei, G. Yuan, S. Longlong, ‘Research on path planning of mobile robot based on improved ant colony algorithm,’ Chinese Journal of Scientific Instrument, pp. 113–121, 2019.
